Your reviewers should be judging the record, not reconstructing it
Visium builds the review layer around GMP production. Executed records checked against the master and the process data, deviations opened with their precedent and their missing context already surfaced, tech transfer packages assembled from what the sending site actually did. Every flag points to the record, the step and the version behind it, so review time goes to judgement instead of reconstruction.
The manufacturing decision problem
Ask where the hours go in a deviation investigation and a large share of the answer is reconstruction. The report arrives missing the detail that mattered, and someone spends the following week recovering it from the people who were on shift.
Review load scales with the batch, not the team
Every batch produces a record a person has to read against the master before anything moves. More products, more markets and more variations mean more pages.
Deviations that queue
An investigation opens in minutes and closes in weeks. The batch waits while it is open, and the events that matter sit behind the events that do not, because triage happens in the order things arrive.
Context that never reached the system
The operator recorded what the field required. The reasoning, the conditions and the judgement stayed on the floor. By the time QA needs them, the people who had them have worked eleven shifts since.
The same process, recorded four ways
MES, LIMS, historians, ERP, environmental monitoring and paper. Each site records the same operation in its own conventions, so a question spanning two sites becomes a project rather than a query.
And someone will ask you to prove it
Every judgement here is re-examined by a person who was not present. A QP at release, an internal auditor a year later, an inspector who opens the file at the page you least expected. A conclusion that cannot be reconstructed from the record does not count. Provenance is a build requirement in this value stream, not a reporting feature.
What are the most common AI use cases in pharma manufacturing?
AI can help speed up pharma manufacturing. Below are three use cases that carry the clearest cost of delay and the shortest path to a measurable result.
Batch record review and release
Every executed record is checked against the master, the limits and the process data before it reaches a reviewer. Entries that agree move through. Entries that do not arrive with the discrepancy named and the page attached.
A review queue ordered by exception, each flag linked to the record, the step and the data behind it.
Deviation investigation and root cause
A new deviation opens with its own history attached: comparable events, what was concluded, what the CAPA was and whether it held. The system also names what the report does not say, so the gaps are visible on day one rather than in week two.
A drafted investigation with precedent, prior root causes and contradicting evidence surfaced, every citation openable, and an explicit list of what is missing from the record.
Tech transfer documentation
Build the package from what the sending site actually did, including the deviations, the changes and the reasoning behind the current parameters.
A package with rationale attached to each parameter, and a named list of what the sending site knows that the document set does not say.
Reading and evidence
Environmental monitoring anomaly detection. Surface excursions and drift against monitoring history, with contributing conditions named.
CAPA management
Track actions, owners and effectiveness evidence, and show where an action closed without evidence behind it.
Process optimisation (golden batch)
Compare executed batches against the runs that performed, and identify which parameters actually separate them.
Prediction and planning
These run on process and business data rather than your document estate. The provenance model is different and we say so in scope.
Yield and scrap prediction
Estimate loss early enough in the run for the intervention to be worth making.
Predictive maintenance
Move interventions from the calendar to the condition of the asset.
Scheduling and capacity optimisation
Sequence campaigns against changeover, capacity and release constraints across lines and sites.
Demand-linked production forecasting
Connect the production plan to demand signal rather than to last quarter's plan.
What we bring to a discovery programme
Discovery teams do not need another general model. They need scientific judgement encoded in something reviewable, running on their own data, inside their own environment.
Validation is the first design constraint
Your quality function sees the intended use, the risk assessment and the test approach before development starts. That is what makes an output usable in a release decision.
Built by a team that works on this full time
Manufacturing is a dedicated product team at Visium, not a vertical applied after the fact. The people building it spend their time on the question of whether an operator halfway through a shift, or a QA manager preparing for an inspection, actually changes what they do because of the system.
Records read at estate scale, with accuracy measured
Handwritten, multilingual and inconsistent records are the normal case, not the exception. We treat accuracy as a stated KPI on the output rather than a property of the model.
Past the pilot
Working with a leading pharmaceutical company we helped put an operating model behind AI across business functions, reducing proof-of-concept execution time by 30% and raising the share of solutions reaching production by more than 50%, across 265+ use cases.
Quality decisions with the data behind them
Discovery buyers ask for validated performance, published methods and named collaborations. Here is what we have on record.
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